A major challenge in systems biology is to quantitatively understand and model the dynamic topological and functional properties of cellular networks, such as the spatial-temporally specific and context-dependent rewiring of transcriptional regulatory circuitry and signal transduction pathways that control cell behavior. Current efforts to study biological networks have primarily focused on creating a descriptive analysis of macroscopic properties. Such simple analyses offer limited insights into the remarkably complex functional and structural organization of a biological system, especially in a dynamic context. Furthermore, most existing techniques for reconstructing molecular networks based on high-throughput data ignore the dynamic aspect of the network topology and represent it as an invariant graph. To our knowledge the network itself is rarely considered as an object that is changing and evolving. In this proposal, we aim to develop principled machine learning algorithms that reverse engineer the temporally and spatially varying interactions between biological molecules from longitudinal or spatial experimental data. Our approaches will take into account biological prior information such as transcriptional factor binding targets, gene knockout experiments, gene ontology, and PPI. Contrary to traditional co-expression studies, our methods unfold the rewiring networks underlying the entire span of the biological process. This will make it possible to discover and trace transient molecular interactions, modules, and pathways during the progression of the process. We will also develop a Bayesian formalism to model and infer the "dynamic network tomography" - the meta-states that determine each molecule's function and relationship to other molecules, thereby driving the evolution of the network topology, possibly in response to internal perturbations or environmental changes. Using these new tools, we will carry out a case study on time series gene expression data from organotypic models of breast cancer progression/reversal to gain insight into the mechanisms that drive the temporal rewiring of gene networks during this process. Finally we will also deliver a software platform offering the tools developed in this project to the public. So far, there has not been work done to consider temporally and spatially varying biological interactions under a unified framework. Our proposed work represents an initial foray into this important problem. Our proposed work represents a significant step forward over the current methodology. We envisage a new paradigm that facilitates: 1) Statistical inference and learning of gene networks that are evolving over space and time, possibly in response to various stimuli and possibly mediating genome-environmental interactions. 2) Thorough exploration of the underlying functional underpinnings that drive the network rewiring, dynamic trajectory, and trend of functional evolution. 3) Uncovering transient events taking place in the dynamic systems, building predictive understanding of the mechanisms of gene regulation, network formation, and evolution. 4) Fast and accurate computational algorithms, with stronger statistical guarantee and greater scalability and robustness in large-scale dynamic network analysis. 5) A full spectrum of convenient software packages and user interfaces for dynamic network analysis, available to the public.